# coding:utf-8
# Author : hiicy redldw
# Date : 2019/05/15
import torch
import torchvision
import torch.nn as nn
import torch.nn.functional as F
import torchvision.transforms as transforms
import torch.optim as optim
# 装饰器，生成器，迭代器 | 描述符，魔法方法如__setitem__，with，| 推导式，元类 ，f表达式
transform = transforms.Compose(
    [transforms.ToTensor(),
     transforms.Normalize((0.5,0.5,0.5),(0.5,0.5,0.5))]
)
trainset = torchvision.datasets.CIFAR10(root='./data', train=True, download=True, transform=transform)
trainloader = torch.utils.data.Dataloader(trainset,batch_size=4,shuffle=True,num_worker=2)
testset = torchvision.datasets.CIFAR10(root='./data', train=False, download=True, transform=transform)
testloader = torch.utils.data.DataLoader(testset, batch_size=4, shuffle=False, num_workers=2)
classes = ('plane', 'car', 'bird', 'cat',
           'deer', 'dog', 'frog', 'horse', 'ship', 'truck')
class Net(nn.Module):
    def __init__(self):
        super(Net, self).__init__()
        self.conv1 = nn.Conv2d(3, 6, 5)
        self.pool = nn.MaxPool2d(2, 2)
        self.conv2 = nn.Conv2d(6, 16, 5)
        self.fc1 = nn.Linear(16 * 5 * 5, 120)
        self.fc2 = nn.Linear(120, 84)
        self.fc3 = nn.Linear(84, 10)

    def forward(self, x):
        x = self.pool(F.relu(self.conv1(x)))
        x = self.pool(F.relu(self.conv2(x)))
        x = x.view(-1, 16 * 5 * 5)
        x = F.relu(self.fc1(x))
        x = F.relu(self.fc2(x))
        x = self.fc3(x)
        return x

net = Net()
criterion = nn.CrossEntropyLoss()
optimizer = optim.SGD(net.parameters(),lr=0.001,momentum=0.9)

for epoch in range(2):
    running_loss = 0.0
    for i,data in enumerate(trainloader,0):
        inputs,labels = data
        # zero the parameter gradients
        optimizer.zero_grad()
        outputs = net(inputs)
        loss = criterion(outputs,labels)
        loss.backward()  # 损失反向传播
        optimizer.step()
        # zero the parameter gradients
        # print statistics
        # REW: torch里的损失返回的都是平均值
        running_loss += loss.item()  # item 转换成python的值
        if i % 2000 == 1999:  # print every 2000 mini-batches
            print('[%d, %5d] loss: %.3f' % (epoch + 1, i + 1, running_loss / 2000))
            running_loss = 0.0

print("Finished Training")

# test
correct = 0
total = 0
with torch.no_grad():
    for data in testloader:
        images, labels = data
        outputs = net(images)
        _,predicted = torch.max(outputs,1)
        total += labels.size(0) # 这是一个批次
        correct+=(predicted==labels).sum().item()
print('Accuracy of the network on the 10000 test images: %d %%' % (
    100 * correct / total))